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TI’s HOG implementation details
BOP setup and assumptions
• ROI size is 240 x 160 (entire down-sampled image)
• Block sizes within ROI are 64x96
• Blocks are further divided into Cells,
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Each of 12x12
Each of 16x16
Each of 24x24
Each of 32x32
• Assumptions:
– Each cell gives a feature
– Smaller cells give better features, so fewer larger cells are considered
in the blocks
– As blocks are part of larger ROI, looks like pixels from adjacent ROIs
are considered in some cases where cells cross block boundaries
Each ROI is divided into Blocks as
shown, skip amount 20 pixels
Each block is divided into Cells
(each of size 12x12), skip amount 8 pixels
Each block is divided into Cells
(each of size 16x16), skip amount 8 pixels
Each block is divided into Cells
(each of size 24x24), skip amount 8 pixels
Each block is divided into Cells
(each of size 32x32), skip amount 8 pixels
Notes
• Note that for smaller cell sizes we compute more
features
• For larger cell sizes, fewer features are computed
• I think, this is because smaller cell sizes give
better feature.
• Also, some cell blocks cross the block boundary,
this is also probably ignored as the block is part of
a larger ROI and the pixels are still available and
valid.
Notes
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•
•
•
For each block we get
756+756+432+180 = 2124 features
There are 36 such blocks
Classifier will use these features and do binary
classification with 51 coefficients
• All 36 blocks will be considered to decide
whether an object of interest is present in the
field of view or not.